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1. Discovering genotype–phenotype relationships with machine learning and the Visual Physiology Opsin Database ( VPOD )

   https://doi.org/10.1093/gigascience/giae073  

   Frazer, Seth_A ; Baghbanzadeh, Mahdi ; Rahnavard, Ali ; Crandall, Keith_A ; Oakley, Todd_H ( October 2024 , GigaScience)

   Predicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenotypic changes has been a goal of decades of experimental work, especially for some model gene families, including light-sensitive opsin proteins. Opsins can be expressed in vitro to measure light absorption parameters, including λmax—the wavelength of maximum absorbance—which strongly affects organismal phenotypes like color vision. Despite extensive research on opsins, the data remain dispersed, uncompiled, and often challenging to access, thereby precluding systematic and comprehensive analyses of the intricate relationships between genotype and phenotype.

   Here, we report a newly compiled database of all heterologously expressed opsin genes with λmax phenotypes that we call the Visual Physiology Opsin Database (VPOD). VPOD_1.0 contains 864 unique opsin genotypes and corresponding λmax phenotypes collected across all animals from 73 separate publications. We use VPOD data and deepBreaks to show regression-based machine learning (ML) models often reliably predict λmax, account for nonadditive effects of mutations on function, and identify functionally critical amino acid sites.

   The ability to reliably predict functions from gene sequences alone using ML will allow robust exploration of molecular-evolutionary patterns governing phenotype, will inform functional and evolutionary connections to an organism’s ecological niche, and may be used more broadly for de novo protein design. Together, our database, phenotype predictions, and model comparisons lay the groundwork for future research applicable to families of genes with quantifiable and comparable phenotypes.

    

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2. Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD)

   https://doi.org/10.1101/2024.02.12.579993  

   Frazer, Seth A ; Baghbanzadeh, Mahdi ; Rahnavard, Ali ; Crandall, Keith A ; Oakley, Todd H ( February 2024 , bioRxiv)

   Predicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenotypic changes has been a goal of decades of experimental work, especially for some model gene families including light-sensitive opsin proteins. Opsins can be expressed in vitro to measure light absorption parameters, including λmax - the wavelength of maximum absorbance - which strongly affects organismal phenotypes like color vision. Despite extensive research on opsins, the data remain dispersed, uncompiled, and often challenging to access, thereby precluding systematic and comprehensive analyses of the intricate relationships between genotype and phenotype.

   Here, we report a newly compiled database of all heterologously expressed opsin genes with λ_maxphenotypes called the Visual Physiology Opsin Database (VPOD).VPOD_1.0contains 864 unique opsin genotypes and corresponding λ_maxphenotypes collected across all animals from 73 separate publications. We useVPODdata anddeepBreaksto show regression-based machine learning (ML) models often reliably predict λ_max, account for non-additive effects of mutations on function, and identify functionally critical amino acid sites.

   The ability to reliably predict functions from gene sequences alone using ML will allow robust exploration of molecular-evolutionary patterns governing phenotype, will inform functional and evolutionary connections to an organism’s ecological niche, and may be used more broadly forde-novoprotein design. Together, our database, phenotype predictions, and model comparisons lay the groundwork for future research applicable to families of genes with quantifiable and comparable phenotypes.

   We introduce the Visual Physiology Opsin Database (VPOD_1.0), which includes 864 unique animal opsin genotypes and corresponding λ_maxphenotypes from 73 separate publications.

   We demonstrate that regression-based ML models can reliably predict λmax from gene sequence alone, predict non-additive effects of mutations on function, and identify functionally critical amino acid sites.

   We provide an approach that lays the groundwork for future robust exploration of molecular-evolutionary patterns governing phenotype, with potential broader applications to any family of genes with quantifiable and comparable phenotypes.

    

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3. The evolution of red color vision is linked to coordinated rhodopsin tuning in lycaenid butterflies

   https://doi.org/10.1073/pnas.2008986118  

   Liénard, Marjorie A. ; Bernard, Gary D. ; Allen, Andrew ; Lassance, Jean-Marc ; Song, Siliang ; Childers, Richard Rabideau ; Yu, Nanfang ; Ye, Dajia ; Stephenson, Adriana ; Valencia-Montoya, Wendy A. ; et al ( February 2021 , Proceedings of the National Academy of Sciences)

   Color vision has evolved multiple times in both vertebrates and invertebrates and is largely determined by the number and variation in spectral sensitivities of distinct opsin subclasses. However, because of the difficulty of expressing long-wavelength (LW) invertebrate opsins in vitro, our understanding of the molecular basis of functional shifts in opsin spectral sensitivities has been biased toward research primarily in vertebrates. This has restricted our ability to address whether invertebrate G_qprotein-coupled opsins function in a novel or convergent way compared to vertebrate G_topsins. Here we develop a robust heterologous expression system to purify invertebrate rhodopsins, identify specific amino acid changes responsible for adaptive spectral tuning, and pinpoint how molecular variation in invertebrate opsins underlie wavelength sensitivity shifts that enhance visual perception. By combining functional and optophysiological approaches, we disentangle the relative contributions of lateral filtering pigments from red-shifted LW and blue short-wavelength opsins expressed in distinct photoreceptor cells of individual ommatidia. We use in situ hybridization to visualize six ommatidial classes in the compound eye of a lycaenid butterfly with a four-opsin visual system. We show experimentally that certain key tuning residues underlying green spectral shifts in blue opsin paralogs have evolved repeatedly among short-wavelength opsin lineages. Taken together, our results demonstrate the interplay between regulatory and adaptive evolution at multiple G_qopsin loci, as well as how coordinated spectral shifts in LW and blue opsins can act together to enhance insect spectral sensitivity at blue and red wavelengths for visual performance adaptation.

    

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4. Short-wavelength-sensitive 2 (Sws2) visual photopigment models combined with atomistic molecular simulations to predict spectral peaks of absorbance

   https://doi.org/https://doi.org/10.1371/journal.pcbi.1008212  

   Patel, Dharmeshkumar ; Barnes, Jonathan E. ; Davies, Wayne I. ; Stenkamp, Deborah L. ; Patel, Jagdish S. ( October 2020 , PLoS computational biology)

   null (Ed.)

   For many species, vision is one of the most important sensory modalities for mediating essential tasks that include navigation, predation and foraging, predator avoidance, and numerous social behaviors. The vertebrate visual process begins when photons of the light interact with rod and cone photoreceptors that are present in the neural retina. Vertebrate visual photopigments are housed within these photoreceptor cells and are sensitive to a wide range of wavelengths that peak within the light spectrum, the latter of which is a function of the type of chromophore used and how it interacts with specific amino acid residues found within the opsin protein sequence. Minor differences in the amino acid sequences of the opsins are known to lead to large differences in the spectral peak of absorbance (i.e. the λmax value). In our prior studies, we developed a new approach that combined homology modeling and molecular dynamics simulations to gather structural information associated with chromophore conformation, then used it to generate statistical models for the accurate prediction of λmax values for photopigments derived from Rh1 and Rh2 amino acid sequences. In the present study, we test our novel approach to predict the λmax of phylogenetically distant Sws2 cone opsins. To build a model that can predict the λmax using our approach presented in our prior studies, we selected a spectrally-diverse set of 11 teleost Sws2 photopigments for which both amino acid sequence information and experimentally measured λmax values are known. The final first-order regression model, consisting of three terms associated with chromophore conformation, was sufficient to predict the λmax of Sws2 photopigments with high accuracy. This study further highlights the breadth of our approach in reliably predicting λmax values of Sws2 cone photopigments, evolutionary-more distant from template bovine RH1, and provided mechanistic insights into the role of known spectral tuning sites 

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5. Multidimensional quantitative phenotypic and molecular analysis reveals neomorphic behaviors of p53 missense mutants

   https://doi.org/10.1038/s41523-023-00582-7  

   Pal, Anasuya ; Gonzalez-Malerva, Laura ; Eaton, Seron ; Xu, Chenxi ; Zhang, Yining ; Grief, Dustin ; Sakala, Lydia ; Nwekwo, Lilian ; Zeng, Jia ; Christensen, Grant ; et al ( September 2023 , npj Breast Cancer)

   Mutations in theTP53tumor suppressor gene occur in >80% of the triple-negative or basal-like breast cancer. To test whether neomorphic functions of specificTP53missense mutations contribute to phenotypic heterogeneity, we characterized phenotypes of non-transformed MCF10A-derived cell lines expressing the ten most common missense mutant p53 proteins and observed a wide spectrum of phenotypic changes in cell survival, resistance to apoptosis and anoikis, cell migration, invasion and 3D mammosphere architecture. The p53 mutants R248W, R273C, R248Q, and Y220C are the most aggressive while G245S and Y234C are the least, which correlates with survival rates of basal-like breast cancer patients. Interestingly, a crucial amino acid difference at one position—R273C vs. R273H—has drastic changes on cellular phenotype. RNA-Seq and ChIP-Seq analyses show distinct DNA binding properties of different p53 mutants, yielding heterogeneous transcriptomics profiles, and MD simulation provided structural basis of differential DNA binding of different p53 mutants. Integrative statistical and machine-learning-based pathway analysis on gene expression profiles with phenotype vectors across the mutant cell lines identifies quantitative association of multiple pathways including the Hippo/YAP/TAZ pathway with phenotypic aggressiveness. Further, comparative analyses of large transcriptomics datasets on breast cancer cell lines and tumors suggest that dysregulation of the Hippo/YAP/TAZ pathway plays a key role in driving the cellular phenotypes towards basal-like in the presence of more aggressive p53 mutants. Overall, our study describes distinct gain-of-function impacts on protein functions, transcriptional profiles, and cellular behaviors of different p53 missense mutants, which contribute to clinical phenotypic heterogeneity of triple-negative breast tumors.

    

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